文章摘要
易文娟,孙刘杰,陈志文,张雷洪,王文举.基于聚类分析的光谱重建样本分析[J].包装工程,2019,40(17):249-255.
YI Wen-juan,SUN Liu-jie,CHEN Zhi-wen,ZHANG Lei-hong,WANG Wen-ju.Spectral Reconstruction Sample Analysis Based on Clustering Analysis[J].Packaging Engineering,2019,40(17):249-255.
基于聚类分析的光谱重建样本分析
Spectral Reconstruction Sample Analysis Based on Clustering Analysis
投稿时间:2019-01-21  修订日期:2019-09-10
DOI:10.19554/j.cnki.1001-3563.2019.17.036
中文关键词: 光谱反射率  主成分分析法  训练样本选择  聚类分析
英文关键词: spectral reflectance  principal component analysis (PCA)  training sample selection  clustering analysis
基金项目:上海市科学技术委员会科研计划(18060502500)
作者单位
易文娟 1.上海理工大学上海 200093 
孙刘杰 1.上海理工大学上海 200093 
陈志文 2.上海城建职业学院上海 201415 
张雷洪 1.上海理工大学上海 200093 
王文举 1.上海理工大学上海 200093 
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中文摘要:
      目的 解决目前光谱重建中因数量大而出现的冗余和繁重等问题,证明聚类算法可以很好地应用在光谱选择样本分析中,并可以实现较高的重构色度精度和物理精度。方法 采用主成分分析法进行仿真实验,首先探究主成分个数,再确定聚类个数,然后比较聚类方法和3种常用的样本选择方法,最后分析比较光源种类对重构结果的影响。结果 通过实验确定主成分个数为6且聚类个数为20时,在A光源下使用KFCM算法的重构效果最好,此时平均色差为0.35ΔE00,平均RMSE为0.0078,平均GFC为99.94%。结论 聚类方法可以应用于光谱成像过程中训练样本选择过程,且有助于提高光谱重构的运算速度和精度。
英文摘要:
      The work aims to solve the problem of redundancy and heaviness caused by the large number of factors in current spectral reconstruction, so as to prove that the clustering algorithm can be well applied in spectral selection sample analysis, and achieve higher reconstruction chromaticity accuracy and physical accuracy. PCA method was used to carry out simulation experiments. Firstly, the number of principal components was explored and the clustering number was determined. Then, the clustering method and three common sample selection methods were compared. Finally, the effects of types of light source on the reconstruction results were analyzed and compared. When the number of principal components determined by experiment was 6 and the clustering number was 20, the reconstruction effect using KFCM algorithm under light source A was the best. At this time, the mean color difference was 0.35ΔE00, the mean RMSE was 0.0078, and the mean GFC was 99.94%. Clustering algorithm can be well applied to the selection of training samples in spectral imaging process, and help improve the computing speed and accuracy of spectral reconstruction.
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